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How do I Select Counts with Spark SQL Without Getting Errors?


By : Schumi
Date : September 15 2020, 09:00 PM
wish of those help I am trying to do a very simple select statement to count the amount of iPod data that is null in my table in spark. My table looks like this , Try this:
code :
apl_df.select("iPod").filter("iPod is null").count()
apl_df.createOrReplaceTempView("apl_tbl")


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Is there a way to create key based on counts in Spark


By : SkyMarshal
Date : March 29 2020, 07:55 AM
may help you . How about using zipWithIndex?

Must include log4J, but it is causing errors in Apache Spark shell. How to avoid errors?


By : Lucky Banana
Date : March 29 2020, 07:55 AM
should help you out The answer is not to use just the :cp command but to also to add the include everything in .../spark/conf/spark-env.sh under the export SPARK_SUBMIT_CLASSPATH=".../the/path/to/a.jar"

What is the reason for compilation errors if different version of Spark-core and Spark-mllib are mixed?


By : Sawsan Jaradat
Date : March 29 2020, 07:55 AM
should help you out I am copying and pasting the exact Spark MLlib LDA example from here: http://spark.apache.org/docs/latest/mllib-clustering.html#latent-dirichlet-allocation-lda , First, code compiles fine. Things I used for setup:
./build.sbt
code :
name := "SO_20150917"

version := "1.0"

scalaVersion := "2.11.7"

libraryDependencies ++= Seq(
  "org.apache.spark"     %% "spark-core"    % "1.5.0",
  "org.apache.spark"     %% "spark-mllib"   % "1.5.0"
)
package somefun

import org.apache.spark.SparkContext
import org.apache.spark.SparkConf
import org.apache.spark.mllib.clustering.{LDA, DistributedLDAModel}
import org.apache.spark.mllib.linalg.Vectors

object Example {
  def main(args: Array[String]) {
    val conf = new SparkConf().setAppName("sample_SBT").setMaster("local[2]")
    val sc = new SparkContext(conf)
    // Load and parse the data
    val data = sc.textFile("data/mllib/sample_lda_data.txt")
    val parsedData = data.map(s => Vectors.dense(s.trim.split(' ').map(_.toDouble)))
    // Index documents with unique IDs
    val corpus = parsedData.zipWithIndex.map(_.swap).cache()

    // Cluster the documents into three topics using LDA
    val ldaModel = new LDA().setK(3).run(corpus)

    // Output topics. Each is a distribution over words (matching word count vectors)
    println("Learned topics (as distributions over vocab of " + ldaModel.vocabSize + " words):")
    val topics = ldaModel.topicsMatrix
    for (topic <- Range(0, 3)) {
      print("Topic " + topic + ":")
      for (word <- Range(0, ldaModel.vocabSize)) { print(" " + topics(word, topic)); }
      println()
    }

    // Save and load model.
    ldaModel.save(sc, "myLDAModel")
    val sameModel = DistributedLDAModel.load(sc, "myLDAModel")
  }
}
[error] (run-main-0) org.apache.hadoop.mapred.InvalidInputException: Input path does not exist: file:/home/martin/IdeaProjects/SO_20150917/data/mllib/sample_lda_data.txt

Spark 2.2 Scala DataFrame select from string array, catching errors


By : Waheeda
Date : March 29 2020, 07:55 AM
will help you I'm new to SparkSQL/Scala and I'm struggling with a couple seemingly simple tasks. , You can just use variadic arguments:
code :
val df = Seq(("a", "1", "c"), ("foo", "bar", "baz")).toDF("a", "b", "c")
val typedCols = Array("a", "cast(b as int) b", "c")
df.selectExpr(typedCols: _*).show

+---+----+---+
|  a|   b|  c|
+---+----+---+
|  a|   1|  c|
|foo|null|baz|
+---+----+---+
val typedCols = Array($"a", $"b" cast "int", $"c")
df.select(typedCols: _*).show
val result = df.selectExpr(typedCols: _*)
val good = result.na.drop()
import org.apache.spark.sql.functions.col

val bad = result.where(result.columns.map(col(_).isNull).reduce(_ || _))
df.where(typedCols.map(_.isNull).reduce(_ || _))  
import org.apache.spark.sql.functions.expr

df.where(typedCols.map(expr(_).isNull).reduce(_ || _))  

Spark: Ignoring or handling DataSet select errors


By : Boncouer
Date : March 29 2020, 07:55 AM
hope this fix your issue Answering my own question based on what I have learned. There are couple of ways to solve it. Spark provides options to ignore corrupt files and corrupt records.
To ignore corrupt files one can set following flag to true:
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